todo:
- new exclusion criteria
- ideal learner stuff
- look at target reaction time?
- import the complexity norm?
Visualizing raw trial looking time
d_no_target <- d %>%
filter(trial_stimulus_type != "target")Looking time distribution
RAW
data_with_demog %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
xlim(0, 9000)## Warning: Removed 72 rows containing non-finite values (stat_bin).## Warning: Removed 2 rows containing missing values (geom_bar).
data_with_demog %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram() +
scale_x_log10()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
AFTER EXCLUSION
d %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram() +
scale_x_log10()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#
# d %>%
# ggplot(aes(x = trial_looking_time)) +
# geom_histogram(bins = 90) +
# xlim(0, 6000) +
# facet_wrap(~subject)Digging through the second hump
- Something wacky is happening in background trials.
- Deviant trials are slower
- target trials are likely a mixture between hits and misses
d %>%
ggplot(aes(x = trial_looking_time,
fill = trial_pressed_space_bar)) +
geom_histogram() +
scale_x_log10() +
facet_wrap(~trial_stimulus_type)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Zoom in on background trials. The “hump” (second mode) is there in all parts of the experiment.
d %>%
mutate(block_num = ceiling(total_trial_num / 48)) %>%
filter(trial_stimulus_type == "background") %>%
ggplot(aes(x = trial_looking_time,
fill = trial_stimulus_complexity)) +
geom_histogram() +
scale_x_log10() +
facet_wrap(~block_num)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
d %>%
filter(trial_stimulus_type == "background") %>%
group_by(subject) %>%
mutate(mlog = mean(log(trial_looking_time)),
sdlog = sd(log(trial_looking_time)),
outlier = log(trial_looking_time) > mlog + 3*sdlog |
log(trial_looking_time) < mlog - 3*sdlog) %>%
ggplot(aes(x = trial_looking_time, fill = outlier)) +
geom_histogram() +
scale_x_log10() +
facet_wrap(~subject)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
We see this pattern within subs to some extent, irrespective of outliers. Is it after target press?
No, it seems that even on background trials where there was not a target beforehand.
d %>%
group_by(subject) %>%
mutate(t_to_b = c(FALSE, diff(as.numeric(as.factor(trial_stimulus_type == "target"))) == -1) & trial_stimulus_type == "background",
d_to_b = c(FALSE, diff(as.numeric(as.factor(trial_stimulus_type == "deviant"))) == -1) & trial_stimulus_type == "background") %>%
filter(trial_stimulus_type == "background") %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram() +
scale_x_log10() +
facet_grid(d_to_b~t_to_b)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
just look at the best of trials of each individual
d %>%
group_by(subject) %>%
mutate(mlog = mean(log(trial_looking_time)),
sdlog = sd(log(trial_looking_time)),
best_trials = log(trial_looking_time) < mlog + 2*sdlog &&
log(trial_looking_time) > mlog - 2*sdlog) %>%
filter(best_trials == TRUE) %>%
ggplot(aes(x = trial_looking_time, fill = trial_stimulus_complexity)) +
geom_histogram() +
scale_x_log10() ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## block type by complexity
d %>%
group_by(subject) %>%
mutate(mlog = mean(log(trial_looking_time)),
sdlog = sd(log(trial_looking_time)),
best_trials = log(trial_looking_time) < mlog + 2*sdlog &&
log(trial_looking_time) > mlog - 2*sdlog) %>%
filter(best_trials == TRUE) %>%
ggplot(aes(x = trial_looking_time, fill = trial_stimulus_complexity)) +
geom_histogram() +
scale_x_log10() +
facet_wrap(~block)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
looks like participants in the mixed_simple_deviant conditions are doing something different. note that mixed simple deviant blocks have complex stimuli as background and simple stimuli as deviant. When they keep seeing complex stuff, then suddenly seeing simple trials. the looking time distribution is different.
Is it because of the outlier exclusion criteria?
d %>%
group_by(subject) %>%
ggplot(aes(x = trial_looking_time, fill = trial_stimulus_complexity)) +
geom_histogram() +
scale_x_log10() +
facet_wrap(~block)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
doesn’t seem to be the case! ok, let’s zoom in on the mixed_simple_deviant block, what’s going on there? am I missing trials?
d %>%
group_by(block, trial_stimulus_complexity) %>%
summarise(
num_trial = n()
) %>%
ggplot(aes(x = block, y = num_trial)) +
geom_point()## `summarise()` regrouping output by 'block' (override with `.groups` argument)
oh well they have similar number of trials! what could happen? Can I find the same pattern in weird deviant simple in each participant?
d %>%
filter(block == "mixed_simple_deviant") %>%
ggplot(aes(x = trial_looking_time, fill = trial_stimulus_complexity)) +
geom_histogram() +
scale_x_log10() +
facet_wrap(~subject)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
d %>%
filter(block == "mixed_complex_deviant") %>%
ggplot(aes(x = trial_looking_time, fill = trial_stimulus_complexity)) +
geom_histogram() +
scale_x_log10() +
facet_wrap(~subject)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
age really has nothing to do with it
d %>%
mutate(
m_age = mean(demog_age, na.rm = TRUE),
age_group = if_else(demog_age < m_age | demog_age == m_age, "young", "old")
) %>%
filter(!is.na(age_group)) %>%
ggplot(aes(x = trial_looking_time, fill = age_group)) +
geom_histogram() +
scale_x_log10() ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
complexity difference
d %>%
ggplot(aes(x = trial_looking_time,
fill = trial_stimulus_complexity),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000)## Warning: Removed 94 rows containing non-finite values (stat_density).
d %>%
ggplot(aes(x = trial_looking_time,
fill = trial_stimulus_complexity),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000) +
facet_wrap(~subject)## Warning: Removed 94 rows containing non-finite values (stat_density).
block difference
d %>%
ggplot(aes(x = trial_looking_time,
fill = block),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000)## Warning: Removed 94 rows containing non-finite values (stat_density).
d %>%
ggplot(aes(x = trial_looking_time,
fill = block),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000) +
facet_wrap(~subject)## Warning: Removed 94 rows containing non-finite values (stat_density).
Complexity differences no target
d_no_target %>%
ggplot(aes(x = trial_looking_time,
fill = trial_stimulus_complexity),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000)## Warning: Removed 64 rows containing non-finite values (stat_density).
d_no_target %>%
ggplot(aes(x = trial_looking_time,
fill = trial_stimulus_complexity),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000) +
facet_wrap(~subject)## Warning: Removed 64 rows containing non-finite values (stat_density).
Block differences no target
d_no_target %>%
ggplot(aes(x = trial_looking_time,
fill = block),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000)## Warning: Removed 64 rows containing non-finite values (stat_density).
d_no_target %>%
ggplot(aes(x = trial_looking_time,
fill = block),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000) +
facet_wrap(~subject)## Warning: Removed 64 rows containing non-finite values (stat_density).## Warning: Groups with fewer than two data points have been dropped.## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning -
## Inf
Visualizing aggregarded looking time
d_sum_individual <- d %>%
group_by(subject, block) %>%
summarise(
mean_lt = mean(trial_looking_time, na.rm = TRUE),
sd = sd(trial_looking_time, na.rm = TRUE),
n = n(),
ci_range_95 = qt(1 - (0.05 / 2), n - 1) * (sd/sqrt(n)),
ci_ub = mean_lt + ci_range_95,
ci_lb = mean_lt - ci_range_95
)## `summarise()` regrouping output by 'subject' (override with `.groups` argument)d_sum <- d %>%
group_by(block) %>%
summarise(
mean_lt = mean(trial_looking_time, na.rm = TRUE),
sd = sd(trial_looking_time, na.rm = TRUE),
n = n(),
ci_range_95 = qt(1 - (0.05 / 2), n - 1) * (sd/sqrt(n)),
ci_ub = mean_lt + ci_range_95,
ci_lb = mean_lt - ci_range_95
)## `summarise()` ungrouping output (override with `.groups` argument)aggregated
d_sum %>% ggplot(aes(x = block, y = mean_lt)) +
geom_pointrange(aes(ymin = ci_lb, ymax = ci_ub)) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
individual
something weird happened
d_sum_individual %>%
ggplot(aes(x = block, y = mean_lt)) +
geom_pointrange(aes(ymin = ci_lb, ymax = ci_ub)) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
d_sum_individual %>%
ggplot(aes(x = block, y = mean_lt)) +
geom_pointrange(aes(ymin = ci_lb, ymax = ci_ub)) +
facet_wrap(~subject) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
weird ones
We can see three very weird ones: SS1604513317537 SS1604515995769 SS1604516882157
weird <- c("SS1604513317537",
"SS1604515995769",
"SS1604516660396")
d_sum_individual %>%
filter(subject %in% weird) %>%
ggplot(aes(x = block, y = mean_lt)) +
geom_pointrange(aes(ymin = ci_lb, ymax = ci_ub)) +
facet_wrap(~subject) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
d %>%
filter(subject %in% weird) %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
facet_wrap(~subject) +
xlim(0, 6000)+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))## Warning: Removed 2 rows containing non-finite values (stat_bin).## Warning: Removed 6 rows containing missing values (geom_bar).
Excluding weird ones
d_no_weird <- d %>%
filter(!(subject %in% weird))
d_no_weird_sum <- d_no_weird %>%
group_by(block) %>%
summarise(
mean_lt = mean(trial_looking_time, na.rm = TRUE),
sd = sd(trial_looking_time, na.rm = TRUE),
n = n(),
ci_range_95 = qt(1 - (0.05 / 2), n - 1) * (sd/sqrt(n)),
ci_ub = mean_lt + ci_range_95,
ci_lb = mean_lt - ci_range_95
)## `summarise()` ungrouping output (override with `.groups` argument)d_no_weird_sum %>% ggplot(aes(x = block, y = mean_lt)) +
geom_pointrange(aes(ymin = ci_lb, ymax = ci_ub)) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
Demographic
SES
education == “Some high school” ~ 1, education == “High school diploma” ~ 2, education == “Associate Degree/Technical certification” ~ 3, education == “Bachelor’s Degree” ~ 4, education == “Master’s Degree” ~ 5, education == “Doctorate/Professional degree” ~ 6, education == “Other” ~ NA_real_
d %>%
distinct(subject, .keep_all = TRUE) %>%
ggplot(aes(x = demog_education)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 4 rows containing non-finite values (stat_bin).
null_m <- lmer(trial_looking_time ~ 1 + (1|subject),
data = filter(d, !is.na(demog_education)))
edu_m <- lmer(trial_looking_time ~ demog_education + (1|subject),
data = filter(d, !is.na(demog_education)))
summary(edu_m)## Linear mixed model fit by REML ['lmerMod']
## Formula: trial_looking_time ~ demog_education + (1 | subject)
## Data: filter(d, !is.na(demog_education))
##
## REML criterion at convergence: 139982.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.940 -0.136 -0.087 -0.027 32.089
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 272043 521.6
## Residual 8451381 2907.1
## Number of obs: 7448, groups: subject, 38
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 174.8 490.0 0.357
## demog_education 197.5 124.5 1.587
##
## Correlation of Fixed Effects:
## (Intr)
## demog_edctn -0.983anova(null_m, edu_m)## refitting model(s) with ML (instead of REML)## Data: filter(d, !is.na(demog_education))
## Models:
## null_m: trial_looking_time ~ 1 + (1 | subject)
## edu_m: trial_looking_time ~ demog_education + (1 | subject)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## null_m 3 140013 140034 -70004 140007
## edu_m 4 140012 140040 -70002 140004 2.5686 1 0.109Age
d %>%
distinct(subject, .keep_all = TRUE) %>%
ggplot(aes(x = demog_age)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 4 rows containing non-finite values (stat_bin).
null_m <- lmer(trial_looking_time ~ 1 + (1|subject),
data = filter(d, !is.na(demog_age)))
age_m <- lmer(trial_looking_time ~ demog_age + (1|subject),
data = filter(d, !is.na(demog_age)))
summary(age_m)## Linear mixed model fit by REML ['lmerMod']
## Formula: trial_looking_time ~ demog_age + (1 | subject)
## Data: filter(d, !is.na(demog_age))
##
## REML criterion at convergence: 139990.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.938 -0.139 -0.086 -0.025 32.086
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 293140 541.4
## Residual 8451383 2907.1
## Number of obs: 7448, groups: subject, 38
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 853.827 286.498 2.980
## demog_age 2.296 7.309 0.314
##
## Correlation of Fixed Effects:
## (Intr)
## demog_age -0.945anova(null_m, age_m)## refitting model(s) with ML (instead of REML)## Data: filter(d, !is.na(demog_age))
## Models:
## null_m: trial_looking_time ~ 1 + (1 | subject)
## age_m: trial_looking_time ~ demog_age + (1 | subject)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## null_m 3 140013 140034 -70004 140007
## age_m 4 140015 140043 -70003 140007 0.104 1 0.7471number of background repetition on looking time
full, aggregated, by complexity
full_aggregated <- d %>%
mutate(
number = 1) %>%
group_by(
subject, block, trial_stimulus_type
) %>%
mutate(num_times_stimulus_seen = cumsum(number))
full_aggregated %>%
filter(trial_stimulus_type == "background") %>%
ggplot(
aes(y = log(trial_looking_time),
x = num_times_stimulus_seen,
color = trial_stimulus_complexity)
) +
geom_point(aes(alpha = 0.2), size = 3, shape = ".") +
guides(alpha = FALSE) +
labs(color = "Stimulus Complexity") +
ylab("Mean Looking Time (log)") +
xlab("Number of Stimulus Reptitions") +
geom_smooth(method = "lm") +
theme(axis.text = element_text(size = 10))## `geom_smooth()` using formula 'y ~ x'
full, aggregated, by blocks
full_aggregated %>%
filter(trial_stimulus_type == "background") %>%
ggplot(
aes(y = log(trial_looking_time),
x = num_times_stimulus_seen,
color = block)
) +
geom_point(aes(alpha = 0.2), size = 3, shape = ".") +
guides(alpha = FALSE) +
labs(color = "Stimulus Complexity") +
ylab("Mean Looking Time (log)") +
xlab("Number of Stimulus Reptitions") +
geom_smooth(method = "lm") +
theme(axis.text = element_text(size = 10))## `geom_smooth()` using formula 'y ~ x'
no weird, aggregated, by complexity
excluded_sum <- full_aggregated %>%
filter(trial_stimulus_type == "background") %>%
filter(!(subject %in% weird))
excluded_sum %>%
ggplot(
aes(y = log(trial_looking_time),
x = num_times_stimulus_seen,
color = trial_stimulus_complexity)
) +
geom_point(aes(alpha = 0.2), size = 3, shape = ".") +
guides(alpha = FALSE) +
labs(color = "Stimulus Complexity") +
ylab("Mean Looking Time (log)") +
xlab("Number of Stimulus Reptitions") +
geom_smooth(method = "lm") +
theme(axis.text = element_text(size = 10))## `geom_smooth()` using formula 'y ~ x'
no weird, aggregated, by blocks
excluded_sum %>%
ggplot(
aes(y = log(trial_looking_time),
x = num_times_stimulus_seen,
color = block)
) +
geom_point(aes(alpha = 0.2), size = 3, shape = ".") +
guides(alpha = FALSE) +
labs(color = "Stimulus Complexity") +
ylab("Mean Looking Time (log)") +
xlab("Number of Stimulus Reptitions") +
geom_smooth(method = "lm") +
theme(axis.text = element_text(size = 10))## `geom_smooth()` using formula 'y ~ x'
individual, by complexity
full_aggregated %>%
filter(trial_stimulus_type == "background") %>%
ggplot(
aes(y = log(trial_looking_time),
x = num_times_stimulus_seen,
color = trial_stimulus_complexity)
) +
geom_point(aes(alpha = 0.2), size = 3, shape = ".") +
guides(alpha = FALSE) +
labs(color = "Stimulus Complexity") +
ylab("Mean Looking Time (log)") +
xlab("Number of Stimulus Reptitions") +
geom_smooth(method = "lm") +
theme(axis.text = element_text(size = 10)) +
facet_wrap(~subject)## `geom_smooth()` using formula 'y ~ x'
individual, by block
full_aggregated %>%
filter(trial_stimulus_type == "background") %>%
ggplot(
aes(y = log(trial_looking_time),
x = num_times_stimulus_seen,
color = block)
) +
geom_point(aes(alpha = 0.2), size = 3, shape = ".") +
guides(alpha = FALSE) +
labs(color = "Stimulus Complexity") +
ylab("Mean Looking Time (log)") +
xlab("Number of Stimulus Reptitions") +
geom_smooth(method = "lm") +
theme(axis.text = element_text(size = 10)) +
facet_wrap(~subject)## `geom_smooth()` using formula 'y ~ x'
Model
linear
block
null_m <- lmer(trial_looking_time ~ 1 + (1|subject),
data = d)
basic_m <- lmer(trial_looking_time ~ block + (1|subject),
data = d)
summary(basic_m)## Linear mixed model fit by REML ['lmerMod']
## Formula: trial_looking_time ~ block + (1 | subject)
## Data: d
##
## REML criterion at convergence: 151943
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.108 -0.127 -0.067 -0.016 33.208
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 568448 754
## Residual 13515244 3676
## Number of obs: 7888, groups: subject, 42
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 939.86 142.80 6.582
## blockall_simple 233.59 116.02 2.013
## blockmixed_complex_deviant 34.13 116.79 0.292
## blockmixed_simple_deviant 64.13 118.66 0.540
##
## Correlation of Fixed Effects:
## (Intr) blckl_ blckmxd_c_
## blckll_smpl -0.406
## blckmxd_cm_ -0.409 0.497
## blckmxd_sm_ -0.397 0.489 0.490anova(null_m, basic_m)## refitting model(s) with ML (instead of REML)## Data: d
## Models:
## null_m: trial_looking_time ~ 1 + (1 | subject)
## basic_m: trial_looking_time ~ block + (1 | subject)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## null_m 3 151999 152020 -75996 151993
## basic_m 6 152000 152042 -75994 151988 4.7832 3 0.1884stimuli repetition
null_m <- lmer(log(trial_looking_time) ~ 1 + (1|subject),
data = excluded_sum)
rep_m <- lmer(log(trial_looking_time) ~ num_times_stimulus_seen + (1|subject),
data = excluded_sum)
summary(rep_m)## Linear mixed model fit by REML ['lmerMod']
## Formula: log(trial_looking_time) ~ num_times_stimulus_seen + (1 | subject)
## Data: excluded_sum
##
## REML criterion at convergence: 9108.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3206 -0.4424 -0.1691 0.1589 9.2370
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.1305 0.3613
## Residual 0.3031 0.5505
## Number of obs: 5434, groups: subject, 39
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.3491570 0.0598278 106.12
## num_times_stimulus_seen -0.0043827 0.0007391 -5.93
##
## Correlation of Fixed Effects:
## (Intr)
## nm_tms_stm_ -0.222anova(null_m, rep_m)## refitting model(s) with ML (instead of REML)## Data: excluded_sum
## Models:
## null_m: log(trial_looking_time) ~ 1 + (1 | subject)
## rep_m: log(trial_looking_time) ~ num_times_stimulus_seen + (1 | subject)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## null_m 3 9133.5 9153.3 -4563.7 9127.5
## rep_m 4 9100.4 9126.8 -4546.2 9092.4 35.056 1 3.204e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1stimuli repetition and block interaction
interaction_m <- lmer(log(trial_looking_time) ~ num_times_stimulus_seen * block + (1|subject),
data = excluded_sum)
summary(interaction_m)## Linear mixed model fit by REML ['lmerMod']
## Formula: log(trial_looking_time) ~ num_times_stimulus_seen * block + (1 |
## subject)
## Data: excluded_sum
##
## REML criterion at convergence: 9147.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3465 -0.4503 -0.1655 0.1712 9.2622
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.1306 0.3614
## Residual 0.3028 0.5502
## Number of obs: 5434, groups: subject, 39
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.342578 0.065381 97.010
## num_times_stimulus_seen -0.005310 0.001475 -3.601
## blockall_simple 0.073070 0.043044 1.698
## blockmixed_complex_deviant -0.013373 0.043044 -0.311
## blockmixed_simple_deviant -0.033754 0.043133 -0.783
## num_times_stimulus_seen:blockall_simple -0.001964 0.002085 -0.942
## num_times_stimulus_seen:blockmixed_complex_deviant 0.002592 0.002085 1.243
## num_times_stimulus_seen:blockmixed_simple_deviant 0.003113 0.002094 1.487
##
## Correlation of Fixed Effects:
## (Intr) nm_t__ blckl_ blckmxd_c_ blckmxd_s_ nm___:_
## nm_tms_stm_ -0.406
## blckll_smpl -0.329 0.617
## blckmxd_cm_ -0.329 0.617 0.500
## blckmxd_sm_ -0.328 0.615 0.499 0.499
## nm_tms_s_:_ 0.287 -0.707 -0.872 -0.436 -0.435
## nm_tms_stmls_sn:blckmxd_c_ 0.287 -0.707 -0.436 -0.872 -0.435 0.500
## nm_tms_stmls_sn:blckmxd_s_ 0.286 -0.704 -0.434 -0.434 -0.871 0.498
## nm_tms_stmls_sn:blckmxd_c_
## nm_tms_stm_
## blckll_smpl
## blckmxd_cm_
## blckmxd_sm_
## nm_tms_s_:_
## nm_tms_stmls_sn:blckmxd_c_
## nm_tms_stmls_sn:blckmxd_s_ 0.498anova(null_m, interaction_m)## refitting model(s) with ML (instead of REML)## Data: excluded_sum
## Models:
## null_m: log(trial_looking_time) ~ 1 + (1 | subject)
## interaction_m: log(trial_looking_time) ~ num_times_stimulus_seen * block + (1 |
## interaction_m: subject)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## null_m 3 9133.5 9153.3 -4563.7 9127.5
## interaction_m 10 9100.9 9166.9 -4540.4 9080.9 46.575 7 6.755e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1GAM
only focusing on looking time at the background
library(mgcv)## Loading required package: nlme##
## Attaching package: 'nlme'## The following object is masked from 'package:lme4':
##
## lmList## The following object is masked from 'package:dplyr':
##
## collapse## This is mgcv 1.8-33. For overview type 'help("mgcv-package")'.gam_d <- full_aggregated %>%
filter(trial_stimulus_type == "background") %>%
mutate(trial_stimulus_complexity = as.factor(trial_stimulus_complexity),
block = as.factor(block))number of repetition
gam_m <- gam(trial_looking_time ~ s(num_times_stimulus_seen),
data = gam_d,
method = "REML")
summary(gam_m)##
## Family: gaussian
## Link function: identity
##
## Formula:
## trial_looking_time ~ s(num_times_stimulus_seen)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 917.30 56.23 16.31 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(num_times_stimulus_seen) 1.057 1.111 0.32 0.643
##
## R-sq.(adj) = -0.000115 Deviance explained = 0.00725%
## -REML = 55035 Scale est. = 1.7822e+07 n = 5636plot(gam_m,
pages = 1,
se = TRUE,
shade = TRUE)
gam.check(gam_m)
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0001684747,-5.863759e-05]
## (score 55035.44 & scale 17821988).
## Hessian positive definite, eigenvalue range [0.001528264,2817].
## Model rank = 10 / 10
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(num_times_stimulus_seen) 9.00 1.06 1.01 0.71concurvity(gam_m, full = TRUE)## para s(num_times_stimulus_seen)
## worst 1.113898e-26 1.124554e-26
## observed 1.113898e-26 5.090767e-31
## estimate 1.113898e-26 3.587588e-29number of repetition (with log?)
gam_m <- gam(log(trial_looking_time) ~ s(num_times_stimulus_seen),
data = gam_d,
method = "REML")
summary(gam_m)##
## Family: gaussian
## Link function: identity
##
## Formula:
## log(trial_looking_time) ~ s(num_times_stimulus_seen)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.270316 0.008667 723.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(num_times_stimulus_seen) 7.001 8.072 12.81 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0181 Deviance explained = 1.93%
## -REML = 5591.4 Scale est. = 0.4234 n = 5636plot(gam_m,
pages = 1,
se = TRUE,
shade = TRUE)
gam.check(gam_m)
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0005601243,-3.726007e-07]
## (score 5591.437 & scale 0.4233963).
## Hessian positive definite, eigenvalue range [1.99837,2817.004].
## Model rank = 10 / 10
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(num_times_stimulus_seen) 9 7 1.03 0.99concurvity(gam_m, full = TRUE)## para s(num_times_stimulus_seen)
## worst 1.113898e-26 1.124554e-26
## observed 1.113898e-26 6.851835e-29
## estimate 1.113898e-26 3.587588e-29number of repetition by stimulus complexity
gam_m <- gam(trial_looking_time ~ s(num_times_stimulus_seen,
by = trial_stimulus_complexity),
data = gam_d,
method = "REML")
summary(gam_m)##
## Family: gaussian
## Link function: identity
##
## Formula:
## trial_looking_time ~ s(num_times_stimulus_seen, by = trial_stimulus_complexity)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 917.31 56.21 16.32 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex 1.036 1.070 3.789
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple 1.360 1.634 0.739
## p-value
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex 0.0503 .
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple 0.3521
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.000686 Deviance explained = 0.111%
## -REML = 55027 Scale est. = 1.7808e+07 n = 5636plot(gam_m,
pages = 1,
se = TRUE,
shade = TRUE)
gam.check(gam_m)
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.01454695,0.01786747]
## (score 55027.21 & scale 17807718).
## Hessian positive definite, eigenvalue range [0.0142575,2816.482].
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex 9.00 1.04 0.99
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple 9.00 1.36 0.99
## p-value
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex 0.10
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple 0.11concurvity(gam_m, full = TRUE)## para
## worst 1.207005e-05
## observed 1.207005e-05
## estimate 1.207005e-05
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex
## worst 6.119153e-06
## observed 3.148278e-08
## estimate 5.550473e-07
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple
## worst 5.950974e-06
## observed 1.834908e-07
## estimate 5.413397e-07number of repetition by stimulus complexity (log?)
gam_m <- gam(log(trial_looking_time) ~ s(num_times_stimulus_seen,
by = trial_stimulus_complexity),
data = gam_d,
method = "REML")
summary(gam_m)##
## Family: gaussian
## Link function: identity
##
## Formula:
## log(trial_looking_time) ~ s(num_times_stimulus_seen, by = trial_stimulus_complexity)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.270357 0.008666 723.6 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex 5.158 6.269 4.719
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple 6.463 7.606 10.020
## p-value
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex 5.71e-05 ***
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0185 Deviance explained = 2.05%
## -REML = 5597 Scale est. = 0.42323 n = 5636plot(gam_m,
pages = 1,
se = TRUE,
shade = TRUE)
gam.check(gam_m)
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.0002553821,-2.062741e-10]
## (score 5597.005 & scale 0.423228).
## Hessian positive definite, eigenvalue range [0.8220389,2816.504].
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex 9.00 5.16 0.98
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple 9.00 6.46 0.98
## p-value
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex 0.04 *
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple 0.05 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1concurvity(gam_m, full = TRUE)## para
## worst 1.207005e-05
## observed 1.207005e-05
## estimate 1.207005e-05
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex
## worst 6.119153e-06
## observed 1.516816e-06
## estimate 5.550473e-07
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple
## worst 5.950974e-06
## observed 1.684933e-06
## estimate 5.413397e-07number of repetition by block
gam_m <- gam(trial_looking_time ~ s(num_times_stimulus_seen, by = block),
data = gam_d,
method = "REML")
plot(gam_m, all.terms = TRUE, pages = 1,
shade = TRUE,
rug = TRUE,
se = TRUE)
summary(gam_m)##
## Family: gaussian
## Link function: identity
##
## Formula:
## trial_looking_time ~ s(num_times_stimulus_seen, by = block)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 917.45 56.21 16.32 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F
## s(num_times_stimulus_seen):blockall_complex 1.345 1.612 4.193
## s(num_times_stimulus_seen):blockall_simple 1.009 1.018 1.646
## s(num_times_stimulus_seen):blockmixed_complex_deviant 1.064 1.126 0.121
## s(num_times_stimulus_seen):blockmixed_simple_deviant 1.050 1.097 0.180
## p-value
## s(num_times_stimulus_seen):blockall_complex 0.0444 *
## s(num_times_stimulus_seen):blockall_simple 0.1971
## s(num_times_stimulus_seen):blockmixed_complex_deviant 0.7547
## s(num_times_stimulus_seen):blockmixed_simple_deviant 0.6922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.000711 Deviance explained = 0.15%
## -REML = 55014 Scale est. = 1.7807e+07 n = 5636gam.check(gam_m)
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.01500944,0.01919385]
## (score 55014.2 & scale 17807260).
## Hessian positive definite, eigenvalue range [0.00108231,2815.481].
## Model rank = 37 / 37
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(num_times_stimulus_seen):blockall_complex 9.00 1.35 1 0.21
## s(num_times_stimulus_seen):blockall_simple 9.00 1.01 1 0.18
## s(num_times_stimulus_seen):blockmixed_complex_deviant 9.00 1.06 1 0.14
## s(num_times_stimulus_seen):blockmixed_simple_deviant 9.00 1.05 1 0.23concurvity(gam_m, full = TRUE)## para s(num_times_stimulus_seen):blockall_complex
## worst 4.313787e-05 6.092671e-06
## observed 4.313787e-05 5.126272e-06
## estimate 4.313787e-05 4.633189e-06
## s(num_times_stimulus_seen):blockall_simple
## worst 5.199012e-06
## observed 2.897025e-06
## estimate 2.704647e-06
## s(num_times_stimulus_seen):blockmixed_complex_deviant
## worst 1.180826e-05
## observed 3.280353e-06
## estimate 2.557590e-06
## s(num_times_stimulus_seen):blockmixed_simple_deviant
## worst 2.003918e-05
## observed 5.720030e-06
## estimate 7.292117e-06number of repetition by block (log?)
gam_m <- gam(log(trial_looking_time) ~ s(num_times_stimulus_seen, by = block),
data = gam_d,
method = "REML")
plot(gam_m, all.terms = TRUE, pages = 1,
shade = TRUE,
rug = TRUE,
se = TRUE)
summary(gam_m)##
## Family: gaussian
## Link function: identity
##
## Formula:
## log(trial_looking_time) ~ s(num_times_stimulus_seen, by = block)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.27042 0.00867 723.3 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F
## s(num_times_stimulus_seen):blockall_complex 2.463 3.068 4.672
## s(num_times_stimulus_seen):blockall_simple 6.443 7.587 8.832
## s(num_times_stimulus_seen):blockmixed_complex_deviant 3.332 4.136 4.069
## s(num_times_stimulus_seen):blockmixed_simple_deviant 2.302 2.869 1.355
## p-value
## s(num_times_stimulus_seen):blockall_complex 0.00267 **
## s(num_times_stimulus_seen):blockall_simple < 2e-16 ***
## s(num_times_stimulus_seen):blockmixed_complex_deviant 0.00228 **
## s(num_times_stimulus_seen):blockmixed_simple_deviant 0.21364
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0176 Deviance explained = 2.02%
## -REML = 5603.3 Scale est. = 0.4236 n = 5636gam.check(gam_m)
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [5.879741e-11,5.306652e-06]
## (score 5603.29 & scale 0.4235977).
## Hessian positive definite, eigenvalue range [0.04763123,2815.503].
## Model rank = 37 / 37
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(num_times_stimulus_seen):blockall_complex 9.00 2.46 1.02 0.94
## s(num_times_stimulus_seen):blockall_simple 9.00 6.44 1.02 0.94
## s(num_times_stimulus_seen):blockmixed_complex_deviant 9.00 3.33 1.02 0.93
## s(num_times_stimulus_seen):blockmixed_simple_deviant 9.00 2.30 1.02 0.98concurvity(gam_m, full = TRUE)## para s(num_times_stimulus_seen):blockall_complex
## worst 4.313787e-05 6.092671e-06
## observed 4.313787e-05 3.784249e-06
## estimate 4.313787e-05 4.633189e-06
## s(num_times_stimulus_seen):blockall_simple
## worst 5.199012e-06
## observed 1.156366e-06
## estimate 2.704647e-06
## s(num_times_stimulus_seen):blockmixed_complex_deviant
## worst 1.180826e-05
## observed 1.124140e-06
## estimate 2.557590e-06
## s(num_times_stimulus_seen):blockmixed_simple_deviant
## worst 2.003918e-05
## observed 1.607399e-05
## estimate 7.292117e-06within subject difference